The invention relates to a method for reading document entries and addresses.
Reading systems have the task of converting text parts, which may also contain numbers, on a document or a dispatch into the equivalent electronic text within a specific application context, and of deriving the specific information which is important in this application. Examples of reading systems are address readers, whose task is to recognize the characters and numbers from the text parts on postal dispatches, such as letters or packages, specifically from the area of the recipient address, and to derive the distribution code from the set of characters and numbers.
For this purpose, a range of recognition methods are used which, once the document or dispatch has been scanned, convert the resultant electronic image into electronic text step by step. The substeps are, specifically, layout analysis (recognition of document type, determination of the regions of interest (ROI), segmentation of the text image into lines, words and/or characters, character classification or recognition of joined-up handwriting, and finally interpretation of the text parts. Each recognition step has a defined parameter set which determines how the subsidiary recognition object is achieved.
To achieve a prescribed reading object, a reading system's recognition steps, and hence the reading system itself, are adapted to the specific characteristics of said reading object in order to attain the best possible reading result. To this end, example documents and dispatches are composed (random samples) which best describe the requirements of the reading object. Each document is provided with the correct solution (label) in the form of the text which is to be extracted, so that a database of pairs of document images and the desired result (label) is built up. This desired result also includes the results of the reading substeps.
On the basis of the prior art, the parameters of the individual recognition steps are set before the actual reading mode such that the reading object, represented by the labeled random sample, is achieved as well as possible. This process, called adaptation, is iterative.
The 3 steps below are cycled through until the recognition performance is satisfactory:                a parameter setting is chosen, the reading system is tested using the random sample images, the results are evaluated using the desired results or labels provided.        
Following adaptation, the reading system has been optimized for processing the elements of the random sample. The composition of the random sample therefore determines to a high degree the recognition performance of the reading system, particularly since the parameter configuration ascertained for the recognition steps during operation is retained for each reading system delivered. Since it is not possible to foresee the actual distribution of the documents to be read for a specific site, the reading system is not optimally adapted to the reading object at a specific location. In particular, local peculiarities and changes in the distribution of the material being read over time cannot be taken into account by the reading system.
Reading systems currently in use do not have the characteristic that they use the documents currently being processed to adapt themselves dynamically to the existing characteristics during operation. The individual recognition steps are always adapted once to prescribed static random samples in advance, as described, and the parameter sets derived therefrom are kept constant in the application. For this purpose, there are a multiplicity of character recognition methods which are adapted to the object to be achieved using a prescribed labeled learning random sample. [Schürmann, Jürgen: Pattern Classification, Wiley Interscience, 1996]. Adaptation algorithms are likewise known for methods for recognizing joined-up handwriting [Rabiner, Lawrence R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition Proceedings of the IEEE, Vol. 77, No. 2, February 1989], as there are for the classification of formula types. Besides these optimization approaches which solve a classification problem, methods are known which optimize the parameters by “specific experimentation”, such as evolutionary algorithms, simulated annealing or the like [Rumelhart, D. E. et al.: Learning Internal Representation by Error Propagation, Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, Mass., 1986/Press, Will H. et al.: Numerical Reciped in C, Chapter 10, Minimization or Maximization of Functions, Cambridge University Press, 1992]. Applications for this purpose are topology optimization in neural networks used for character classification, for example. When using these methods, however, a previously defined random sample is always used from which the optimized parameter sets are calculated, which are not altered again in the application.